{"id":5445,"date":"2022-10-10T08:43:24","date_gmt":"2022-10-10T06:43:24","guid":{"rendered":"https:\/\/equinoxgroup.eu\/?p=5445"},"modified":"2024-04-24T02:22:22","modified_gmt":"2024-04-24T00:22:22","slug":"alphafold-ai-predicts-shape-of-nearly-every-organism-with-protein-sequence-data","status":"publish","type":"post","link":"https:\/\/equinoxgroup.eu\/alphafold-ai-predicts-shape-of-nearly-every-organism-with-protein-sequence-data\/","title":{"rendered":"AlphaFold AI predicts the shape of nearly every organism with protein sequence data"},"content":{"rendered":"<div class=\"wpb-content-wrapper\"><p>[vc_row el_class=&#8221;vc_hidden-lg vc_hidden-md&#8221;][vc_column][vc_empty_space height=&#8221;190px&#8221;][vc_empty_space height=&#8221;130px&#8221; el_class=&#8221;vc_hidden-sm vc_hidden-xs&#8221;][\/vc_column][\/vc_row][vc_row css=&#8221;.vc_custom_1667979542523{margin-top: -320px !important;margin-bottom: 419px !important;}&#8221; el_class=&#8221;vc_hidden-sm vc_hidden-xs&#8221;][vc_column][vc_row_inner][vc_column_inner][vc_column_text]<span data-offset-key=\"be20s-15-0\">Alpha<\/span><span data-offset-key=\"be20s-16-0\">F<\/span><span data-offset-key=\"be20s-17-0\">old<\/span><span data-offset-key=\"be20s-18-0\"> is<\/span><span data-offset-key=\"be20s-19-0\"> a<\/span><span data-offset-key=\"be20s-20-0\"> computer<\/span><span data-offset-key=\"be20s-21-0\"> program<\/span><span data-offset-key=\"be20s-22-0\"> that<\/span><span data-offset-key=\"be20s-23-0\"> accurately<\/span><span data-offset-key=\"be20s-24-0\"> predicts<\/span><span data-offset-key=\"be20s-25-0\"> the<\/span><span data-offset-key=\"be20s-26-0\"> three<\/span><span data-offset-key=\"be20s-27-0\">&#8211;<\/span><span data-offset-key=\"be20s-28-0\">dimensional<\/span><span data-offset-key=\"be20s-29-0\"> shapes<\/span><span data-offset-key=\"be20s-30-0\"> of<\/span><span data-offset-key=\"be20s-31-0\"> proteins<\/span><span data-offset-key=\"be20s-32-0\">.<\/span><span data-offset-key=\"be20s-33-0\"> It<\/span><span data-offset-key=\"be20s-34-0\"> was<\/span><span data-offset-key=\"be20s-35-0\"> developed<\/span><span data-offset-key=\"be20s-36-0\"> by<\/span><span data-offset-key=\"be20s-37-0\"> Google<\/span><span data-offset-key=\"be20s-38-0\"> Deep<\/span><span data-offset-key=\"be20s-39-0\">Mind<\/span><span data-offset-key=\"be20s-40-0\">,<\/span><span data-offset-key=\"be20s-41-0\"> in<\/span><span data-offset-key=\"be20s-42-0\"> collaboration<\/span><span data-offset-key=\"be20s-43-0\"> with<\/span><span data-offset-key=\"be20s-44-0\"> Oxford<\/span><span data-offset-key=\"be20s-45-0\"> University<\/span><span data-offset-key=\"be20s-46-0\"> and<\/span><span data-offset-key=\"be20s-47-0\"> the<\/span><span data-offset-key=\"be20s-48-0\"> European<\/span><span data-offset-key=\"be20s-49-0\"> Molecular<\/span><span data-offset-key=\"be20s-50-0\"> Biology<\/span><span data-offset-key=\"be20s-51-0\"> Laboratory<\/span><span data-offset-key=\"be20s-52-0\">.<\/span> <span data-offset-key=\"be20s-55-0\">P<\/span><span data-offset-key=\"be20s-56-0\">rote<\/span><span data-offset-key=\"be20s-57-0\">ins<\/span><span data-offset-key=\"be20s-58-0\"> are<\/span><span data-offset-key=\"be20s-59-0\"> essential<\/span><span data-offset-key=\"be20s-60-0\"> biom<\/span><span data-offset-key=\"be20s-61-0\">ole<\/span><span data-offset-key=\"be20s-62-0\">cules<\/span><span data-offset-key=\"be20s-63-0\"> that<\/span><span data-offset-key=\"be20s-64-0\"> perform<\/span><span data-offset-key=\"be20s-65-0\"> a<\/span><span data-offset-key=\"be20s-66-0\"> vast<\/span><span data-offset-key=\"be20s-67-0\"> array<\/span><span data-offset-key=\"be20s-68-0\"> of<\/span><span data-offset-key=\"be20s-69-0\"> functions<\/span><span data-offset-key=\"be20s-70-0\"> in<\/span><span data-offset-key=\"be20s-71-0\"> all<\/span><span data-offset-key=\"be20s-72-0\"> living<\/span><span data-offset-key=\"be20s-73-0\"> organisms and<\/span><span data-offset-key=\"be20s-75-0\"> their<\/span><span data-offset-key=\"be20s-76-0\"> three<\/span><span data-offset-key=\"be20s-77-0\">&#8211;<\/span><span data-offset-key=\"be20s-78-0\">dimensional<\/span><span data-offset-key=\"be20s-79-0\"> structure<\/span><span data-offset-key=\"be20s-80-0\">,<\/span><span data-offset-key=\"be20s-81-0\"> or<\/span><span data-offset-key=\"be20s-82-0\"> con<\/span><span data-offset-key=\"be20s-83-0\">formation (the protein structure)<\/span><span data-offset-key=\"be20s-84-0\">,<\/span><span data-offset-key=\"be20s-85-0\"> is<\/span><span data-offset-key=\"be20s-86-0\"> critical<\/span><span data-offset-key=\"be20s-87-0\"> to<\/span><span data-offset-key=\"be20s-88-0\"> their<\/span><span data-offset-key=\"be20s-89-0\"> function<\/span><span data-offset-key=\"be20s-90-0\">.<\/span><span data-offset-key=\"be20s-91-0\"> However<\/span><span data-offset-key=\"be20s-92-0\">,<\/span><span data-offset-key=\"be20s-93-0\"> predicting<\/span><span data-offset-key=\"be20s-94-0\"> protein<\/span><span data-offset-key=\"be20s-95-0\"> structure<\/span><span data-offset-key=\"be20s-96-0\"> from<\/span><span data-offset-key=\"be20s-97-0\"> sequence<\/span><span data-offset-key=\"be20s-98-0\"> is<\/span><span data-offset-key=\"be20s-99-0\"> one<\/span><span data-offset-key=\"be20s-100-0\"> of<\/span><span data-offset-key=\"be20s-101-0\"> the<\/span><span data-offset-key=\"be20s-102-0\"> most<\/span><span data-offset-key=\"be20s-103-0\"> challenging<\/span><span data-offset-key=\"be20s-104-0\"> problems<\/span><span data-offset-key=\"be20s-105-0\"> in<\/span><span data-offset-key=\"be20s-106-0\"> biology<\/span><span data-offset-key=\"be20s-107-0\">.<\/span> <span data-offset-key=\"be20s-110-0\"> Alpha<\/span><span data-offset-key=\"be20s-111-0\">F<\/span><span data-offset-key=\"be20s-112-0\">old<\/span><span data-offset-key=\"be20s-113-0\"> represents<\/span><span data-offset-key=\"be20s-114-0\"> a<\/span><span data-offset-key=\"be20s-115-0\"> major<\/span><span data-offset-key=\"be20s-116-0\"> advance<\/span><span data-offset-key=\"be20s-117-0\"> in<\/span><span data-offset-key=\"be20s-118-0\"> protein<\/span><span data-offset-key=\"be20s-119-0\"> structure<\/span><span data-offset-key=\"be20s-120-0\"> prediction<\/span><span data-offset-key=\"be20s-121-0\">,<\/span><span data-offset-key=\"be20s-122-0\"> and<\/span><span data-offset-key=\"be20s-123-0\"> will<\/span><span data-offset-key=\"be20s-124-0\"> help<\/span><span data-offset-key=\"be20s-125-0\"> accelerate<\/span><span data-offset-key=\"be20s-126-0\"> progress<\/span><span data-offset-key=\"be20s-127-0\"> in<\/span><span data-offset-key=\"be20s-128-0\"> many<\/span><span data-offset-key=\"be20s-129-0\"> areas<\/span><span data-offset-key=\"be20s-130-0\"> of<\/span><span data-offset-key=\"be20s-131-0\"> basic<\/span><span data-offset-key=\"be20s-132-0\"> research<\/span><span data-offset-key=\"be20s-133-0\"> and<\/span><span data-offset-key=\"be20s-134-0\"> drug<\/span><span data-offset-key=\"be20s-135-0\"> discovery<\/span><span data-offset-key=\"be20s-136-0\">.<\/span>[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row bg_type=&#8221;image&#8221; parallax_style=&#8221;vcpb-hz-jquery&#8221; bg_image_new=&#8221;id^5447|url^https:\/\/equinoxgroup.eu\/wp-content\/uploads\/2022\/11\/Alphafold-Protein.gif|caption^DeepMind\u2019s AlphaFold determines structures of around 200 million proteins|alt^DeepMind\u2019s AlphaFold determines structures of around 200 million proteins|title^DeepMind\u2019s AlphaFold determines structures of around 200 million proteins|description^DeepMind\u2019s AlphaFold determines structures of around 200 million proteins&#8221; bg_image_repeat=&#8221;no-repeat&#8221; bg_image_size=&#8221;contain&#8221;][vc_column][vc_empty_space height=&#8221;220px&#8221;][vc_empty_space height=&#8221;200px&#8221; el_class=&#8221;vc_hidden-xs&#8221;][\/vc_column][\/vc_row][vc_row][vc_column][vc_custom_heading icon=&#8221;fas fa-dna&#8221; text=&#8221;Alphafold AI Predicts the Shape of Nearly Every Organism with Protein Sequence Data&#8221; font_container=&#8221;tag:h1|font_size:32|text_align:left|line_height:1.2&#8243; use_theme_fonts=&#8221;yes&#8221; css_animation=&#8221;fadeInLeft&#8221;][vc_empty_space height=&#8221;20px&#8221;][\/vc_column][\/vc_row][vc_row css=&#8221;.vc_custom_1620565725690{margin-bottom: 20px !important;}&#8221;][vc_column offset=&#8221;vc_col-lg-9 vc_col-md-9&#8243; css=&#8221;.vc_custom_1452702342137{padding-right: 45px !important;}&#8221;][vc_row_inner css=&#8221;.vc_custom_1664315258707{margin-right: 40px !important;background-color: #f2f4fa !important;}&#8221;][vc_column_inner width=&#8221;2\/3&#8243; css=&#8221;.vc_custom_1664317901362{margin-top: 50px !important;}&#8221;]<div class=\"audioplayer-tobe  playerid-5456 ap_idx_5445_1 is-single-player 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data-type=\"audio\" data-source=\"https:\/\/equinoxgroup.eu\/wp-content\/uploads\/2022\/10\/Alphafold-AI-Predicts-The-Shape-Of-Nearly-Every-Organism-With-Protein-Sequence-Data.mp3\" data-playfrom=\"off\"><div hidden class=\"feed-dzsap feed-artist-name\">Algorithmic BrAIn - Alphafold AI Predicts The Shape Of Nearly Every Organism With Protein Sequence Data<\/div><div hidden class=\"feed-dzsap feed-song-name\">Tired of Reading? Listen Instead!<\/div><div class=\"meta-artist track-meta-for-dzsap\"><span class=\"the-artist first-line\"><span class=\"first-line-label\" >Algorithmic BrAIn - Alphafold AI Predicts The Shape Of Nearly Every Organism With Protein Sequence Data<\/span><\/span><span class=\"the-name the-songname second-line\" >Tired of Reading? Listen Instead!<\/span><\/div><div class=\"menu-description\"><span class=\"the-artist\">Algorithmic BrAIn - Alphafold AI Predicts The Shape Of Nearly Every Organism With Protein Sequence Data<\/span><span class=\"the-name\">Tired of Reading? Listen Instead!<\/span><\/div><div hidden class=\"feed-dzsap feed-dzsap--extra-html feed-dzsap--extra-html--bottom-left \"><span class=\"btn-zoomsounds btn-like\"><span class=\"the-icon\"><svg xmlns:svg=\"http:\/\/www.w3.org\/2000\/svg\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" version=\"1.0\" width=\"15\" height=\"15\"  viewBox=\"0 0 645 700\" id=\"svg2\"> <defs id=\"defs4\" \/> <g id=\"layer1\"> <path d=\"M 297.29747,550.86823 C 283.52243,535.43191 249.1268,505.33855 220.86277,483.99412 C 137.11867,420.75228 125.72108,411.5999 91.719238,380.29088 C 29.03471,322.57071 2.413622,264.58086 2.5048478,185.95124 C 2.5493594,147.56739 5.1656152,132.77929 15.914734,110.15398 C 34.151433,71.768267 61.014996,43.244667 95.360052,25.799457 C 119.68545,13.443675 131.6827,7.9542046 172.30448,7.7296236 C 214.79777,7.4947896 223.74311,12.449347 248.73919,26.181459 C 279.1637,42.895777 310.47909,78.617167 316.95242,103.99205 L 320.95052,119.66445 L 330.81015,98.079942 C 386.52632,-23.892986 564.40851,-22.06811 626.31244,101.11153 C 645.95011,140.18758 648.10608,223.6247 630.69256,270.6244 C 607.97729,331.93377 565.31255,378.67493 466.68622,450.30098 C 402.0054,497.27462 328.80148,568.34684 323.70555,578.32901 C 317.79007,589.91654 323.42339,580.14491 297.29747,550.86823 z\" id=\"path2417\" style=\"\" \/> <g transform=\"translate(129.28571,-64.285714)\" id=\"g2221\" \/> <\/g> <\/svg><\/span><span class=\"the-label hide-on-active\">Like<\/span><span class=\"the-label show-on-active\">Liked<\/span><\/span><\/div><div hidden class=\"feed-dzsap feed-dzsap--extra-html\" data-playerid=\"5456\" style=\"opacity:0;\"><div class=\"dzsap-counter counter-hits\"><i class=\"fa fa-play\"><\/i><span class=\"the-number\">5<\/span><\/div><div class=\"dzsap-counter counter-likes\"><i class=\"fa fa-heart\"><\/i><span class=\"the-number\">3<\/span><\/div><\/div><\/div>[\/vc_column_inner][vc_column_inner width=&#8221;1\/3&#8243; css=&#8221;.vc_custom_1668098398215{margin-left: 40px !important;border-right-width: 10px !important;border-left-width: 10px !important;padding-top: 20px !important;padding-bottom: 20px !important;background: #153e4d url(https:\/\/equinoxgroup.eu\/wp-content\/uploads\/2021\/06\/Website-Narration.jpg?id=4189) !important;background-position: center !important;background-repeat: no-repeat !important;background-size: cover !important;}&#8221; offset=&#8221;vc_hidden-xs&#8221;][vc_custom_heading icon=&#8221;fas fa-assistive-listening-systems&#8221; icon_size=&#8221;36&#8243; stm_title_font_weight=&#8221;700&#8243; text=&#8221;&#8221; font_container=&#8221;tag:h2|font_size:36|text_align:left|color:%23153e4d|line_height:0.8&#8243; use_theme_fonts=&#8221;yes&#8221; css_animation=&#8221;none&#8221; subtitle=&#8221;Prefer to listen?&#8221; subtitle_color=&#8221;#ffffff&#8221;][vc_empty_space height=&#8221;20px&#8221;][vc_column_text]<\/p>\n<p style=\"text-align: justify;\"><span style=\"color: #ffffff; font-size: 12pt; text-align: justify;\">If you prefer to listen to, instead of reading the text on this page, all you need to do is to put your device sound on, hit the play button on the left,\u00a0 sit back, relax and leave everything else to us.<\/span><\/p>\n<p>[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][vc_column_text css=&#8221;.vc_custom_1667840574354{margin-top: 32px !important;}&#8221;]<\/p>\n<p style=\"text-align: justify;\">DeepMind\u2019s <a href=\"https:\/\/alphafold.ebi.ac.uk\/\" rel=\"nofollow\">AlphaFold<\/a> tool has determined the structures of around 200 million proteins. Knowing the 3D structure of almost every protein known to science will be as easy as doing a Google search from now on.<\/p>\n<p style=\"text-align: justify;\">Researchers have utilised AlphaFold, a groundbreaking Artificial Intelligence (AI) network, to predict the structures of more than 200 million proteins from around 1 million species, representing almost every known protein on Earth.<\/p>\n<p style=\"text-align: justify;\">The data dump is freely-accessible on a database created by DeepMind, the Google-owned London-based AI company that developed AlphaFold, and the European Molecular Biology Laboratory&#8217;s European Bioinformatics Institute (EMBL\u2013EBI), a non-profit organisation located near Cambridge, UK.<\/p>\n<p>[\/vc_column_text][vc_custom_heading icon=&#8221;fas fa-dna&#8221; icon_size=&#8221;36&#8243; text=&#8221;What&#8217;s Next for AlphaFold and the Revolution in AI Protein-Folding?&#8221; font_container=&#8221;tag:h3|font_size:24|text_align:left|line_height:1.2&#8243; use_theme_fonts=&#8221;yes&#8221; css_animation=&#8221;fadeInLeft&#8221; css=&#8221;.vc_custom_1713918138214{margin-bottom: 16px !important;}&#8221;][vc_column_text css=&#8221;.vc_custom_1667289449740{margin-top: 32px !important;}&#8221;]<\/p>\n<p style=\"text-align: justify;\">At a press conference, DeepMind CEO Demis Hassabis said, \u201cEssentially, you can think of it as covering the whole protein universe.\u201d The beginning of a new age of digital biology has arrived.<\/p>\n<p style=\"text-align: justify;\">The cellular function of a protein is determined by its three-dimensional form or structure. Most medications are produced based on structural knowledge, and the construction of precise maps of the amino-acid arrangement of proteins is often the first step in learning how proteins function.<\/p>\n<p style=\"text-align: justify;\">DeepMind created the AlphaFold network using a form of AI known as deep learning, and the AlphaFold database was released a year ago with more than 350,000 structure predictions covering nearly every protein produced by humans, mice, and 19 other organisms with extensive research. The catalogue now has around one million entries and is over 23TeraBytes in size.<\/p>\n<p style=\"text-align: justify;\">Christine Orengo, a computational biologist at University College London who has used the AlphaFold database to uncover novel protein families, says, \u201cWe&#8217;re preparing for the release of this massive treasure.\u201d It&#8217;s nice to have all information forecasted for us.<\/p>\n<p>[\/vc_column_text][vc_custom_heading icon=&#8221;stm-certificate&#8221; icon_size=&#8221;36&#8243; text=&#8221;Quality Structures&#8221; font_container=&#8221;tag:h3|font_size:24|text_align:left|line_height:1.2&#8243; use_theme_fonts=&#8221;yes&#8221; css_animation=&#8221;fadeInLeft&#8221; css=&#8221;.vc_custom_1713918129645{margin-bottom: 16px !important;}&#8221;][vc_column_text css=&#8221;.vc_custom_1667289459972{margin-top: 32px !important;}&#8221;]<\/p>\n<p style=\"text-align: justify;\">Since the publication of AlphaFold a year ago, members of the life-sciences community have been ultra-keen on taking a go at the software. The network generates very precise predictions of the structures of several proteins. It also gives information on the accuracy of its forecasts so that academics may determine if they can be relied upon. X-ray crystallography and cryo-electron microscopy are time-consuming and expensive experimental approaches that have been traditionally used by scientists to determine protein structures.<\/p>\n<p style=\"text-align: justify;\">According to EMBL\u2013EBI, around 35 percent of the more than 214 million predictions are considered to be extremely accurate, meaning that they are comparable to empirically established structures. Another 45% are deemed precise enough for several applications.<\/p>\n<p>[\/vc_column_text][vc_custom_heading icon=&#8221;fas fa-ethernet&#8221; icon_size=&#8221;36&#8243; text=&#8221;DeepMind&#8217;s AI Makes a Huge Advance in Deciphering Protein Structures&#8221; font_container=&#8221;tag:h3|font_size:24|text_align:left|line_height:1.2&#8243; use_theme_fonts=&#8221;yes&#8221; css_animation=&#8221;fadeInLeft&#8221; css=&#8221;.vc_custom_1713918121894{margin-bottom: 16px !important;}&#8221;][vc_column_text css=&#8221;.vc_custom_1667840419044{margin-top: 32px !important;}&#8221;]<\/p>\n<p style=\"text-align: justify;\">Numerous AlphaFold structures are adequate replacements for experimental structures in some applications. In other circumstances, researchers use AlphaFold predictions to evaluate and interpret experimental results. Poor forecasts are often visible, and some of them are caused by inherent disorder in the protein itself, which implies it lacks a definite form \u2014 at least when it is not in the presence of other molecules.<\/p>\n<p style=\"text-align: justify;\">Today&#8217;s 200 million predictions are based on the sequences in another database called UniProt. A computational biologist at the <a href=\"https:\/\/www.carrerasresearch.org\/en\/\">Josep Carreras Leukaemia Research Institute<\/a> (IJC) in Barcelona, Spain, says that it is likely that scientists will already have an idea of the shapes of some of these proteins due to their inclusion in databases of experimental structures or similarity to other proteins in such repositories.<\/p>\n<p style=\"text-align: justify;\">However, such listings tend to favour human, mouse, and other mammalian proteins, according to Porta. Because it contains such a wide variety of creatures, it&#8217;s conceivable that the AlphaFold dump will provide important information. It will be an excellent resource.<\/p>\n<p style=\"text-align: justify;\">Since AlphaFold&#8217;s software has been accessible for a year, scientists can already predict the structure of any protein of their choosing. The availability of forecasts in a single database, according to many, will save researchers time, money, and hassle. \u201cYou are removing another barrier to entrance,&#8221; says Porta. \u201cI&#8217;ve used several AlphaFold models and I have never run AlphaFold myself.\u201d<\/p>\n<p style=\"text-align: justify;\">Jan Kosinski, a structural modeller at <a href=\"https:\/\/www.embl.org\/sites\/hamburg\/\">EMBL Hamburg<\/a> in Germany who has managed the AlphaFold network for the last year, is eagerly anticipating the development of the database. Once, his team spent three weeks estimating the proteome \u2014 the collection of all proteins in an organism \u2014 of a virus. During the briefing, he said, \u201cNow we can just download every model.\u201d<\/p>\n<p>[\/vc_column_text][vc_custom_heading icon=&#8221;fas fa-th&#8221; icon_size=&#8221;36&#8243; text=&#8221;Three Trillion Bytes&#8221; font_container=&#8221;tag:h3|font_size:24|text_align:left|line_height:1.2&#8243; use_theme_fonts=&#8221;yes&#8221; css_animation=&#8221;fadeInLeft&#8221; css=&#8221;.vc_custom_1713918113046{margin-bottom: 16px !important;}&#8221; el_class=&#8221;h2.consulting-custom-title.Post_ClassH2&#8243;][vc_column_text css=&#8221;.vc_custom_1667289476364{margin-top: 32px !important;}&#8221;]<\/p>\n<p style=\"text-align: justify;\">Including almost every known protein in the database will also enable new forms of research. Orengo and her colleagues have utilised the AlphaFold database to find new types of protein families, and they will continue to do so on a much greater scale in the future. Additionally, she and her colleagues will utilise the increased database to better comprehend the development of proteins with advantageous features \u2014 such as the capacity to devour plastic \u2014 or concerning properties, such as the potential to cause cancer. The discovery of these proteins&#8217; distant cousins in the database may identify the origin of their features.<\/p>\n<p style=\"text-align: justify;\">Martin Steinegger, a computational biologist at Seoul National University who helped design a cloud-based version of AlphaFold, is enthusiastic about the database&#8217;s growth. However, he believes that researchers will likely need to operate the network themselves. People are increasingly using AlphaFold to identify how proteins interact, despite the absence of such predictions in the database. The sequencing of genetic material from soil, ocean water, and other so-called \u201cmetagenomic\u201d sources identifies microbial proteins are not on the database either.<\/p>\n<p style=\"text-align: justify;\">Many researchers \u00a0won&#8217;t be able to download the complete 23-terabyte contents of the larger AlphaFold database, which Steinegger believes may be necessary for certain advanced applications and cloud-based storage may be expensive. FoldSeek, a programme co-created by Steinegger, can rapidly identify structurally related proteins and should also be able to significantly compress AlphaFold data.<\/p>\n<p style=\"text-align: justify;\">Even though the AlphaFold database contains almost every known protein, it will need to be updated when new creatures are found. The accuracy of AlphaFold&#8217;s predictions may potentially be enhanced when fresh structural data becomes available. Hassabis asserts that DeepMind has committed to maintaining the database indefinitely and that he anticipates yearly upgrades.<\/p>\n<p style=\"text-align: justify;\">His expectation is that the availability of the AlphaFold database will have an enduring effect on the biological sciences. It will need a substantial shift in mindset.<\/p>\n<p>[\/vc_column_text]<div class=\"templatera_shortcode\"><style type=\"text\/css\" data-type=\"vc_shortcodes-default-css\">.vc_do_custom_heading{margin-bottom:0.625rem;margin-top:0;}.vc_do_custom_heading{margin-bottom:0.625rem;margin-top:0;}.vc_do_btn{margin-bottom:22px;}<\/style><style type=\"text\/css\" data-type=\"vc_shortcodes-custom-css\">.vc_custom_1667111652227{margin-top: 48px !important;}.vc_custom_1713917281413{margin-top: 14px !important;margin-bottom: 42px !important;padding-bottom: 12px !important;}.vc_custom_1667116849933{margin-top: 28px !important;margin-bottom: 28px !important;}.vc_custom_1713917315239{margin-top: 14px !important;margin-bottom: 42px !important;padding-bottom: 12px !important;}<\/style><div class=\"vc_row wpb_row vc_row-fluid vc_custom_1667111652227\"><div class=\"wpb_column vc_column_container vc_col-sm-12\"><div class=\"vc_column-inner \"><div class=\"wpb_wrapper\">\t<div class=\"vc_separator 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class=\"post_bottom media\">\n\t<div class=\"tags media-body\"><a href=\"https:\/\/equinoxgroup.eu\/tag\/ai\/\" rel=\"tag\">AI<\/a> <a href=\"https:\/\/equinoxgroup.eu\/tag\/data-limitations\/\" rel=\"tag\">data limitations<\/a> <a href=\"https:\/\/equinoxgroup.eu\/tag\/machine-learning-approaches\/\" rel=\"tag\">machine learning approaches<\/a><\/div>\t<\/div><\/div><div class=\"about_author_wr \">\n    <\/div>\t<div class=\"vc_separator wpb_content_element vc_sep_width_100 vc_sep_border_width_3 type_1  vc_separator_no_text\">\n\t\t<span class=\"vc_sep_holder vc_sep_holder_l\"><span style=\"border-color:#fde952;\" class=\"vc_sep_line\"><\/span><\/span>\n\t\t\t\t<span class=\"vc_sep_holder vc_sep_holder_r\"><span style=\"border-color:#fde952;\" class=\"vc_sep_line\"><\/span><\/span>\n\t<\/div>\n\t\n    <div class=\"stm_post_comments\">\n            <\/div>\n<\/div><\/div><\/div><\/div><\/div>[\/vc_column][vc_column width=&#8221;1\/4&#8243; offset=&#8221;vc_hidden-sm 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From redefining data quality, to methods like JEPA, data augmentation and transfer learning, our future with AI holds promising solutions. The limitations of scaling AI and the importance of","protected":false},"author":1,"featured_media":5447,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[21,19],"tags":[356,357,358,361,359,360],"insights_perspectives":[],"class_list":{"0":"post-5445","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-algorithmic-brain","8":"category-artificial-intelligence-ai","9":"tag-alphafold","10":"tag-deepmind","11":"tag-google","12":"tag-mapping","13":"tag-protein","14":"tag-protein-structure"},"yoast_head":"\n<title>AlphaFold AI predicts the shape of nearly every organism with protein sequence data - equinoxgroup.eu<\/title>\n<meta name=\"description\" content=\"DeepMind\u2019s AlphaFold has determined the structure of around 200 million proteins, reaching a major milestone in biology research.\" \/>\n<meta name=\"robots\" content=\"index, follow, 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